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When an 8-magnitude earthquake struck Yingjie Hu’s home province of Sichuan, China, in 2008, he was more than 1,000 miles away attending college in Shanghai. While Hu wanted to help, there wasn’t much he could do due to the long distance.



Their paper was recently published in the journal Geographical Analysis.

Grid cells for disaster mapping (left) and cells prioritized using color codes (right).



Hu and his colleagues—Krzysztof Janowicz and Helen Couclelis, both of the University of California, Santa Barbara—developed an algorithm for prioritizing the mapping tasks. Their method takes into account the area’s population, disaster severity, and the road network and simulates potential rescue routes. The priorities of the grid cells are then ranked based on how the information within each cell can potentially assist the route-planning decisions of response teams. The result of the algorithm can help inform online volunteers about the priorities of the grid cells through color codes.

“Different grid cells contain different geographic content,” Hu said. “If online volunteers can first map the grid cells that are more urgent, response teams may be able to use the information at an earlier stage.”


“Online volunteers provide up-to-date geographic information that can help disaster response teams on the ground to make more informed decisions,” he said.

Right now, Hu’s algorithm only focuses on road networks.

“Within one grid, there can be other types of geographic information like hospital capacity or shelters,” Hu said. “Eventually, we could also quantify the value of these other types of geographic information and aggregate them to provide a more comprehensive rank of the grids.”


